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Wnt-deficient and hypoxic environment orchestrates squamous reprogramming of human pancreatic ductal adenocarcinoma

Nature Cell Biology volume 26, pages 1759–1772 (2024)Cite this article

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Abstract

Human pancreatic cancer is characterized by the molecular diversity encompassing native duct-like and squamous cell-like identities, but mechanisms underlying squamous transdifferentiation have remained elusive. To comprehensively capture the molecular diversity of human pancreatic cancer, we here profiled 65 patient-derived pancreatic cancer organoid lines, including six adenosquamous carcinoma lines. H3K27me3-mediated erasure of the ductal lineage specifiers and hijacking of the TP63-driven squamous-cell programme drove squamous-cell commitment, providing survival benefit in a Wnt-deficient environment and hypoxic conditions. Gene engineering of normal pancreatic duct organoids revealed that GATA6 loss and a Wnt-deficient environment, in concert with genetic or hypoxia-mediated inactivation of KDM6A, facilitate squamous reprogramming, which in turn enhances environmental fitness. EZH2 inhibition counterbalanced the epigenetic bias and curbed the growth of adenosquamous cancer organoids. Our results demonstrate how an adversarial microenvironment dictates the molecular and histological evolution of human pancreatic cancer and provide insights into the principles and significance of lineage conversion in human cancer.

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Fig. 1: Morphological and molecular characterization of PASC organoids.
Fig. 2: TP63 is a hub for the squamous cell identity in PASC organoids.
Fig. 3: Epigenetic characterization of PASC organoids.
Fig. 4: KDM6A loss and WR-deficient environment induce squamous reprogramming.
Fig. 5: Hypoxia drives squamous reprogramming.
Fig. 6: Single-cell analysis of transcriptomic changes during hypoxia-induced squamous reprogramming.
Fig. 7: EZH2 inhibition blocks PASC growth in vitro.

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Data availability

Whole-exosome sequencing, RNA-seq, ATAC-seq and ChIP-seq data that support the findings of this study have been deposited in the NBDC Human Database under accession no. JGAS000719. Microarray data that were analysed are deposited in the Gene Expression Omnibus under accession code GSE263236. Whole-genome sequencing data of PC94 and PC95 have been deposited to the European Genome-phenome Archive under the accession no. EGAS00001007211. Raw sequencing data for PC8, PC11, PC12, PC13, PC15, PC16, PC18, PC19, PC20, PC26, PC27, PC31, PC36, PC43, PC44, PC49, PC54 and PC59 are not applicable to deposition, as we could not reach them due to their demise and could not obtain additional informed consent for data depositing.

The human pancreatic cancer data were derived from the ICGC, TCGA Research Network (http://cancergenome.nih.gov/) and previous studies10,37. The datasets that support the findings of this study are available at https://static-content.springer.com/esm/art%3A10.1038%2Fnature16965/MediaObjects/41586_2016_BFnature16965_MOESM271_ESM.xlsx, https://static-content.springer.com/esm/art%3A10.1038%2Fs43018-019-0010-1/MediaObjects/43018_2019_10_MOESM3_ESM.zip and in the Gene Expression Omnibus under accession code GSE93326. All other data supporting the findings of this study are available from the corresponding author on reasonable request. Source data are provided with this paper.

References

  1. Fujii, M., Sekine, S. & Sato, T. Decoding the basis of histological variation in human cancer. Nat. Rev. Cancer 24, 141–158 (2024).

    Article  CAS  PubMed  Google Scholar 

  2. Bailey, P. et al. Genomic analyses identify molecular subtypes of pancreatic cancer. Nature 531, 47–52 (2016).

    Article  CAS  PubMed  Google Scholar 

  3. Collisson, E. A. et al. Subtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy. Nat. Med. 17, 500–503 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Moffitt, R. A. et al. Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma. Nat. Genet. 47, 1168–1178 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Waddell, N. et al. Whole genomes redefine the mutational landscape of pancreatic cancer. Nature 518, 495–501 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Cancer Genome Atlas Research Network. Integrated genomic characterization of pancreatic ductal adenocarcinoma. Cancer Cell 32, 185–203 (2017).

  7. Puleo, F. et al. Stratification of pancreatic ductal adenocarcinomas based on tumor and microenvironment features. Gastroenterology 155, 1999–2013.e3 (2018).

  8. Connor, A. A. et al. Integration of genomic and transcriptional features in pancreatic cancer reveals increased cell cycle progression in metastases. Cancer Cell 35, 267–282.e267 (2019).

  9. Collisson, E. A., Bailey, P., Chang, D. K. & Biankin, A. V. Molecular subtypes of pancreatic cancer. Nat. Rev. Gastroenterol. Hepatol. 16, 207–220 (2019).

    Article  PubMed  Google Scholar 

  10. Hayashi, A. et al. A unifying paradigm for transcriptional heterogeneity and squamous features in pancreatic ductal adenocarcinoma. Nat. Cancer 1, 59–74 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Chan-Seng-Yue, M. et al. Transcription phenotypes of pancreatic cancer are driven by genomic events during tumor evolution. Nat. Genet. 52, 231–240 (2020).

    Article  CAS  PubMed  Google Scholar 

  12. Raghavan, S. et al. Microenvironment drives cell state, plasticity, and drug response in pancreatic cancer. Cell 184, 6119–6137.e6126 (2021).

  13. Miyabayashi, K. et al. Intraductal transplantation models of human pancreatic ductal adenocarcinoma reveal progressive transition of molecular subtypes. Cancer Discov. 10, 1566–1589 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Mueller, S. et al. Evolutionary routes and KRAS dosage define pancreatic cancer phenotypes. Nature 554, 62–68 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Adams, C. R. et al. Transcriptional control of subtype switching ensures adaptation and growth of pancreatic cancer. ELife 8, e45313 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Kapoor, A. et al. Yap1 activation enables bypass of oncogenic Kras addiction in pancreatic cancer. Cell 158, 185–197 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Tu, B. et al. YAP1 oncogene is a context-specific driver for pancreatic ductal adenocarcinoma. JCI Insight 4, e130811 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Martinelli, P. et al. GATA6 regulates EMT and tumour dissemination, and is a marker of response to adjuvant chemotherapy in pancreatic cancer. Gut 66, 1665–1676 (2017).

    Article  CAS  PubMed  Google Scholar 

  19. Seino, T. et al. Human pancreatic tumor organoids reveal loss of stem cell niche factor dependence during disease progression. Cell Stem Cell 22, 454–467 e456 (2018).

    Article  CAS  PubMed  Google Scholar 

  20. Brunton, H. et al. HNF4A and GATA6 loss reveals therapeutically actionable subtypes in pancreatic cancer. Cell Rep. 31, 107625 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Kloesch, B. et al. A GATA6-centred gene regulatory network involving HNFs and DeltaNp63 controls plasticity and immune escape in pancreatic cancer. Gut 71, 766–777 (2022).

    Article  CAS  PubMed  Google Scholar 

  22. Kalisz, M. et al. HNF1A recruits KDM6A to activate differentiated acinar cell programs that suppress pancreatic cancer. EMBO J. 39, e102808 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Andricovich, J. et al. Loss of KDM6A activates super-enhancers to induce gender-specific squamous-like pancreatic cancer and confers sensitivity to BET inhibitors. Cancer Cell 33, 512–526 e518 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Watanabe, S. et al. Loss of KDM6A characterizes a poor prognostic subtype of human pancreatic cancer and potentiates HDAC inhibitor lethality. Int. J. Cancer 145, 192–205 (2019).

    Article  CAS  PubMed  Google Scholar 

  25. Cihak, R. W., Kawashima, T. & Steer, A. Adenoacanthoma (adenosquamous carcinoma) of the pancreas. Cancer 29, 1133–1140 (1972).

    Article  CAS  PubMed  Google Scholar 

  26. Voong, K. R. et al. Resected pancreatic adenosquamous carcinoma: clinicopathologic review and evaluation of adjuvant chemotherapy and radiation in 38 patients. Hum. Pathol. 41, 113–122 (2010).

    Article  CAS  PubMed  Google Scholar 

  27. Boyd, C. A., Benarroch-Gampel, J., Sheffield, K. M., Cooksley, C. D. & Riall, T. S. 415 patients with adenosquamous carcinoma of the pancreas: a population-based analysis of prognosis and survival. J. Surg. Res. 174, 12–19 (2012).

    Article  PubMed  Google Scholar 

  28. Simone, C. G. et al. Characteristics and outcomes of adenosquamous carcinoma of the pancreas. Gastrointest. Cancer Res. 6, 75–79 (2013).

    PubMed  PubMed Central  Google Scholar 

  29. Whissell, G. et al. The transcription factor GATA6 enables self-renewal of colon adenoma stem cells by repressing BMP gene expression. Nat. Cell Biol. 16, 695–707 (2014).

    Article  CAS  PubMed  Google Scholar 

  30. Tsuji, S. et al. The miR-363-GATA6-Lgr5 pathway is critical for colorectal tumourigenesis. Nat. Commun. 5, 3150 (2014).

    Article  PubMed  Google Scholar 

  31. Somerville, T. D. D. et al. TP63-mediated enhancer reprogramming drives the squamous subtype of pancreatic ductal adenocarcinoma. Cell Rep. 25, 1741–1755 e1747 (2018).

  32. Somerville, T. D. et al. Squamous trans-differentiation of pancreatic cancer cells promotes stromal inflammation. ELife 9, e53381 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Hamdan, F. H. & Johnsen, S. A. DeltaNp63-dependent super enhancers define molecular identity in pancreatic cancer by an interconnected transcription factor network. Pro. Natl Acad. Sci. USA 115, E12343–E12352 (2018).

    Article  CAS  Google Scholar 

  34. Chakraborty, A. A. et al. Histone demethylase KDM6A directly senses oxygen to control chromatin and cell fate. Science 363, 1217–1222 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Su, H. T. et al. Stem cell marker nestin is critical for TGF-β1-mediated tumor progression in pancreatic cancer. Mol. Cancer Res. 11, 768–779 (2013).

    Article  CAS  PubMed  Google Scholar 

  36. Papageorgis, P. et al. Smad4 inactivation promotes malignancy and drug resistance of colon cancer. Cancer Res. 71, 998–1008 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Maurer, C. et al. Experimental microdissection enables functional harmonisation of pancreatic cancer subtypes. Gut 68, 1034–1043 (2019).

    Article  CAS  PubMed  Google Scholar 

  38. Park, H. W. et al. Alternative Wnt signaling activates YAP/TAZ. Cell 162, 780–794 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Zorn, A. M. & Wells, J. M. Vertebrate endoderm development and organ formation. Annu. Rev. Cell Dev. Biol. 25, 221–251 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Agger, K. et al. UTX and JMJD3 are histone H3K27 demethylases involved in HOX gene regulation and development. Nature 449, 731–734 (2007).

    Article  CAS  PubMed  Google Scholar 

  41. Su, W. et al. 18F-FDG PET/CT feature of pancreatic adenosquamous carcinoma with pathological correlation. Abdom. Radiol. 45, 743–749.

  42. Losman, J. A., Koivunen, P. & Kaelin, W. G. Jr. 2-Oxoglutarate-dependent dioxygenases in cancer. Nat. Rev. Cancer 20, 710–726 (2020).

    Article  CAS  PubMed  Google Scholar 

  43. Carey, B. W., Finley, L. W., Cross, J. R., Allis, C. D. & Thompson, C. B. Intracellular α-ketoglutarate maintains the pluripotency of embryonic stem cells. Nature 518, 413–416 (2015).

    Article  CAS  PubMed  Google Scholar 

  44. Ezponda, T. et al. UTX/KDM6A loss enhances the malignant phenotype of multiple myeloma and sensitizes cells to EZH2 inhibition. Cell Rep. 21, 628–640 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Ler, L. D. et al. Loss of tumor suppressor KDM6A amplifies PRC2-regulated transcriptional repression in bladder cancer and can be targeted through inhibition of EZH2. Sci. Transl. Med. 9, eaai8312 (2017).

    Article  PubMed  Google Scholar 

  46. Kawasaki, K. et al. An organoid biobank of neuroendocrine neoplasms enables genotype–phenotype mapping. Cell 183, 1420–1435.e1421 (2020).

  47. Park, J. W. et al. Reprogramming normal human epithelial tissues to a common, lethal neuroendocrine cancer lineage. Science 362, 91–95 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Mu, P. et al. SOX2 promotes lineage plasticity and antiandrogen resistance in TP53- and RB1-deficient prostate cancer. Science 355, 84–88 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. https://doi.org/10.14806/ej.17.1.200 (2011).

  51. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  52. Foroutan, M. et al. Single sample scoring of molecular phenotypes. BMC Bioinform. 19, 404 (2018).

    Article  CAS  Google Scholar 

  53. Hao, Y. et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01767-y (2023).

  54. Linderman, G. C. et al. Zero-preserving imputation of single-cell RNA-seq data. Nat. Commun. 13, 192 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genom. 19, 477 (2018).

    Article  Google Scholar 

  56. Van den Berge, K. et al. Trajectory-based differential expression analysis for single-cell sequencing data. Nat. Commun. 11, 1201 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Randolph, L. N., Bao, X., Zhou, C. & Lian, X. An all-in-one, Tet-On 3G inducible PiggyBac system for human pluripotent stem cells and derivatives. Sci. Rep. 7, 1549 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Matano, M. et al. Modeling colorectal cancer using CRISPR-Cas9-mediated engineering of human intestinal organoids. Nat. Med. https://doi.org/10.1038/nm.3802 (2015).

  60. Koo, B. K. et al. Controlled gene expression in primary Lgr5 organoid cultures. Nat. Methods 9, 81–83 (2012).

    Article  CAS  Google Scholar 

  61. Fujii, M., Matano, M., Nanki, K. & Sato, T. Efficient genetic engineering of human intestinal organoids using electroporation. Nat. Protoc. 10, 1474–1485 (2015).

    Article  CAS  PubMed  Google Scholar 

  62. Guo, Q. et al. ‘Cold shock’ increases the frequency of homology directed repair gene editing in induced pluripotent stem cells. Sci. Rep. 8, 2080 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Corces, M. R. et al. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat. Methods 14, 959–962 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Zhang, Y. et al. Model-based analysis of ChIP-seq (MACS). Genome Biol. 9, R137 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Schmidl, C., Rendeiro, A. F., Sheffield, N. C. & Bock, C. ChIPmentation: fast, robust, low-input ChIP-seq for histones and transcription factors. Nat. Methods 12, 963–965 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Shi, X. et al. Integrated profiling of human pancreatic cancer organoids reveals chromatin accessibility features associated with drug sensitivity. Nat. Commun. 13, 2169 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Tiriac, H. et al. Organoid profiling identifies common responders to chemotherapy in pancreatic cancer. Cancer Discov. 8, 1112–1129 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the Collaborative Research Resources, School of Medicine, Keio University for the technical assistance. This work was supported by Japan Science and Technology Agency (JST) (Moonshot R&D) (grant no. JPMJMS2022), JST ERATO (grant no. JPMJER2303), JSPS KAKENHI (grant nos. 16K07148, 19K07739, 22K07241, 22H04995 and 23K27677) and Takeda Science Foundation.

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Author notes

  1. These authors jointly supervised this work: Masayuki Fuji, Toshiro Sato.

Authors and Affiliations

  1. Department of Organoid Medicine, Sakaguchi Laboratory, Keio University School of Medicine, Tokyo, Japan

    Hiroki Tamagawa, Masayuki Fujii, Kazuhiro Togasaki, Takashi Seino, Shintaro Kawasaki, Ai Takano, Kohta Toshimitsu, Sirirat Takahashi, Yuki Ohta, Mami Matano, Kenta Kawasaki, Yujiro Machida & Toshiro Sato

  2. Department of Integrated Medicine and Biochemistry, Keio University School of Medicine, Tokyo, Japan

    Hiroki Tamagawa, Masayuki Fujii, Kazuhiro Togasaki, Shintaro Kawasaki, Ai Takano, Sirirat Takahashi, Yuki Ohta, Mami Matano, Kenta Kawasaki, Yujiro Machida & Toshiro Sato

  3. Department of Gastroenterology, Keio University School of Medicine, Tokyo, Japan

    Hiroki Tamagawa, Kazuhiro Togasaki, Takashi Seino, Shintaro Kawasaki, Kohta Toshimitsu, Kenta Kawasaki, Yujiro Machida, Eisuke Iwasaki, Takanori Kanai & Toshiro Sato

  4. Division of Pathology and Clinical Laboratories, National Cancer Center Hospital, Tokyo, Japan

    Shigeki Sekine

  5. KAN Research Institute, Kobe, Japan

    Akihito Machinaga & Ken Sasai

  6. Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, Kobe, Japan

    Yuzo Kodama

  7. Department of Pathology and Tumor Biology, Kyoto University Graduate School of Medicine, Kyoto, Japan

    Nobuyuki Kakiuchi, Seishi Ogawa & Tomonori Hirano

  8. Department of Gastroenterology and Hepatology, Kyoto University Graduate School of Medicine, Kyoto, Japan

    Nobuyuki Kakiuchi, Tomonori Hirano & Hiroshi Seno

  9. Department of Surgery, Keio University School of Medicine, Tokyo, Japan

    Minoru Kitago & Yuko Kitagawa

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  1. Hiroki Tamagawa
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  2. Masayuki Fujii
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Contributions

H.T., M.F. and T. Sato conceived and designed the study. H.T., M.F., T. Seino, S.K., A.T., S.T., Y.O., M.M., K.K., Y.M., A.M. and K.S. performed the experiments. M.F., K. Togasaki and K. Toshimitsu performed bioinformatics analysis. Y. Kodama., N.K., S.O., T.H., H.S., M.K., Y. Kitagawa, E.I. and T.K. provided clinical samples and advice from a clinical viewpoint. S.S. assessed xenograft histology. H.T., M.F. and T. Sato wrote the paper with input from all authors.

Corresponding authors

Correspondence to Masayuki Fujii or Toshiro Sato.

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Competing interests

T.S. is an inventor on several patents related to organoid culture. All other authors declare no competing interests.

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Nature Cell Biology thanks Meritxell Huch, Mara Sherman and Jens Siveke for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Genomic and transcriptomic features of PC organoids.

a, The genomic landscape of the PC organoid library. Alterations in genes recurrently affected in clinical PC samples are shown on the top. The copy number state of the indicated oncogenes and tumour-suppressor genes (middle), and the chromosomes (bottom). The average copy number of the chromosomes is shown on the bottom right. Solid-type organoid lines are highlighted in pink characters. b, The morphology of solid organoid lines in the presence or absence of Wnt-3A/R-spondin. The organoids were plated as single cells and cultured in the indicated conditions for seven days. Scale bar, 100 μm. Representative data from three independent culture wells with similar results. c, The expression of Moffitt’s Basal-like and Classical genes in PC organoids. The organoids were sorted based on the absolute enrichment of the Basal-like signature (the Basal-like score minus the Classical score), and the genes were ordered according to the spearman’s correlation between the gene expression and the absolute Basal-like signature score. Solid organoid lines are highlighted in pink characters.

Extended Data Fig. 2 Squamous features in PASC organoids.

a, Gene set enrichment analysis of PASC organoids versus non-PASC organoids. The genes were ranked according to the fold change value in differential gene expression analysis, and the enrichment of GOBP gene sets was analysed. Top five gene sets with positive or negative enrichment based on the adjusted P value are shown. NES, normalized enrichment score. b, Micrographs of PASC organoid xenografts. Wnt+ organoid (PC3) is shown for comparison. The PC88 tumour is a orthotopic xenograft, and the others are subcutaneous tumours. The four PASC lines analysed stain positive for squamous cell markers p63 and KRT5/6. Scale bar, 100 μm. c, Hierarchical clustering of ICGC (left) and TCGA (right) samples using the genes differentially expressed between PASC and non-PASC organoids (fold change > 4 or < 0.25, adjusted P value < 0.05 in DESeq). The samples were annotated according to their molecular subtypes, and histology for ICGC tumours. ADEX; aberrantly differentiated exocrine endocrine, IPMN; intraductal papillary mucinous neoplasm. d, Positive correlations between LGR5 and GATA6 expressions in PC organoids, and in the ICGC and TCGA cohorts. PASC organoids and the Squamous subtype tumours show low expressions of LGR5 and GATA6. r, Pearson correlation coefficient. Box plots represent the median (centre line), upper and lower quartiles (box limits), and 1.5 × interquartile ranges (whiskers).

Source data

Extended Data Fig. 3 TP63 perturbation in PC and genetically engineered pancreas organoids.

a, Gene expression microarray analysis of PC24TG-ΔNp63OE and PC24TG organoids. Genes with a fold change value > 4 are shown in red, and those with fold change < 0.25 are show in blue. Representative squamous cell and ductal genes are highlighted. b, Overexpression of ΔNp63 in KCT#1-GATA6KD organoids. Overexpression was induced using a doxycycline-inducible system for this line and confirmed with an immunoassay (bottom). Data from one capillary run. c, Morphological alteration and p63 immunostaining of KCT#1-GATA6KD organoids following ΔNp63 overexpression. Scale bar, 100 μm. Representative data from at least 10 organoids with similar results for each image. d, An MA plot showing gene expression changes by ΔNp63 overexpression in KCT#1-GATA6KD organoids. Genes with fold change > 4 are shown in red, and those with fold change < 0.25 are show in blue. e, Gene enrichment analysis of KCT#1-GATA6KD-ΔNp63OE versus KCT#1-GATA6KD organoids. The enrichment of genes upregulated in PASC organoids (fold change > 4, adjusted P value < 0.05 in DESeq) is shown. NES, normalized enrichment score. f, ΔNp63 overexpression confers WR independency on KCT#1-GATA6KD organoids. g, Induction of AXIN2 following treatment with Wnt/R-spondin in KCT#1-GATA6KD and KCT#1-GATA6KD-ΔNp63OE organoids. Data are shown as mean ± s.e.m. Data from three biological replicates. h, Design for TP63 knockout in the PC35 PASC organoid line and confirmation by Sanger sequencing. Guide RNA target sites are indicated with black triangle and underline. i, An MA plot demonstrating gene expression changes following TP63 knockout in PC35 organoids. Genes with fold change > 4 are shown in red, and those with fold change < 0.25 are show in blue. Selected squamous cell and ductal genes are highlighted.

Source data

Extended Data Fig. 4 ATAC-seq analysis of PC organoids.

a, Hyper- (fold change > 8, adjusted P value < 10−4) and hypo-accessible (fold change < 0.125, adjusted P value < 10−4) ATAC-seq peaks in PASC organoids. The average coverage for each subtype is shown (left). While these peaks are exclusively gained or lost in PASC organoids, WRi PC organoids also show a reduced accessibility at hypo-accessible peaks. Genomic features of the peaks are shown on the right. Promoter refers to genomic loci spanning 3 kb upstream and downstream of the transcription start site. Untranslated region (UTR) shows 5 kb flanking regions of the coding region. b, Gene ontology analysis of hyper- and hypo-accessible ATAC-seq peaks in PASC organoids using GREAT and GOBP terms. For each peakset, top 10 gene sets with more than 10 genes and fold enrichment > 2 are shown. Statistics from the hypergeometric test are presented. c, ATAC-seq coverage at genomic loci with the TP63, HNF1B, or GATA6 JASPAR motif in PASC and non-PASC organoids. d, Altered chromatin accessibility following ΔNp63 overexpression in PC24TG organoids and TP63 knockout in PC35 organoids. ATAC coverage around genomic loc with the TP63 motif, and hyper- and hypo- accessible peaks in PASC organoids are shown.

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Extended Data Fig. 5 Generation and characterization of KDM6A-knockout organoids.

a, Generation of KCT#2. Black triangle and underline show sgRNA targets. KRASG12V knock-in and knockout of CDKN2A and TP53 were confirmed by Sanger sequencing. b, Knockout of GATA6 and/or KDM6A in KCT lines (KCT#1 and KCT#2). Sanger sequencing results for each clone are shown. Guide RNA target sites are indicated with black triangle and underline. c, Confirmation of GATA6 and KDM6A knockout by immunoassay. Data from single capillary runs. d, The growth of KCT-GATA6KO organoids in the indicated conditions. KCT-GATA6KO organoids show minimal growth in the absence of Wnt-3A/R-spondin, and solid organoids did not emerge. Scale bar, 100 μm. Representative data from at least three independent experiments with similar results. e, The growth of KCT#2-GATA6/KDM6ADKO and -KDM6AKO organoids in the presence and absence of WR. Similar to KCT#1, only GATA6/KDM6ADKO organoids gave rise to WR-independent solid organoids. Navy arrowheads show solid organoids that were sporadically visible on day 7. Scale bar, 100 μm. Data from three independent experiments with similar results (left) and a single experiment (right). f, Confirmation of de novo TP63 expression in solid KCT-GATA6/KDM6ADKO organoids by an immunoassay. g, Reduced AXIN2 induction by WR in solid KCT-GATA6/KDM6ADKO organoids compared to cystic counterparts. Data are shown as mean ± s.e.m. Data from three biological replicates. h, Re-exposure of solid KCT#1-GATA6/KDM6ADKO organoids to Wnt/R-Spondin. Wnt/R-spondin re-supplementation did not revert the solid phenotype. Scale bar, 100 μm. Similar results were obtained from three independent experiments.

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Extended Data Fig. 6 Transcriptomic and epigenetic analyses of KDM6AKO organoids.

a, Principal component analysis using the transcriptomes of genetically engineered organoids and PC organoids. Solid KCT-GATA6/KDM6ADKO organoids show a PASC-like transcriptome pattern. b, Principal component analysis using the chromatin accessibility profiles of genetically engineered organoids and PC organoids. Cystic-to-solid transformation of KCT-GATA6/KDM6ADKO organoids accompanies a PASC-like remodelling of the global chromatin accessibility pattern. c, Solid KCT-GATA6/KDM6ADKO organoids undergo H3K27 trimethylation of genes that are repressed by H3K27me3 in PASC organoids (fold change < 0.5, adjusted P value < 0.05 in RNA-seq, and fold change > 2, adjusted P value < 0.05 in H3K27me3 ChIP-seq).

Extended Data Fig. 7 Squamous reprogramming with hypoxia and Wnt/R-spondin deprivation.

a, The expression (left) and enrichment (right) of Hallmark hypoxia genes in the TCGA PAAD dataset. n = 31 (Squamous), 53 (Pancreatic progenitor), 27 (immunogenic) and 38 (ADEX). Box plots represent the median (centre line), upper and lower quartiles (box limits), and 1.5 × interquartile ranges (whiskers). b, Enrichment of 171 hypoxia-associated genes in ICGC and TCGA datasets. Statistics, one-way ANOVA followed by Tukey’s post-hoc test. Box plots represent the median (centre line), upper and lower quartiles (box limits), and 1.5 × interquartile ranges (whiskers). c, Comparative gene expression analysis of hypoxia-cultured KCTS#2-GATA6KO organoids vs the normoxia-cultured counterpart using edgeR. Genes with fold change > 4, adjusted P value < 0.05 are shown in red, and those with fold change < 0.25, adjusted P value < 0.05 are shown in blue. d, The growth and morphology of hypoxia-reprogrammed KCTS#2-GATA6KO organoids following a re-exposure to the indicated conditions (left) and viability measurement with ATP luminescence (right). After 2 months of culture in a Wnt/R-spondin-deficient hypoxic condition and acquisition of PASC features, KCTS#2-GATA6KO organoids were plated as single cells and cultured in the indicated conditions for seven days. Data are shown as mean ± s.d. Each dot represents one well. e, GATA6 expression in the PC organoid RNA-seq data. f–j, The morphology and p63 expression in the indicated lines following a two-month culture in normoxia or hypoxia. Wnt+ lines were also cultured in the presence or absence of R-spondin. Two Wnt+ lines (PC5 and PC29) showed upregulation of p63 in a hypoxic R-spondin-lacking condition, whereas the other lines did not. M, marker. Scale bar, 100 μm. For each image, similar result was obtained at least three times from different time points. Immunoassay data derive from single capillary runs.

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Extended Data Fig. 8 Validation of Wnt and hypoxia signatures in external datasets.

a, Expression of Wnt ligand genes in laser-capture micro-dissected epithelial and stromal components in PC samples. Patients with paired epithelial and stromal samples were selected. The Wnt genes were ordered according to the mean normalized expression in each component. The Pearson correlation coefficient (r) between the PASC signature and Wnt ligand expression is shown on the right. b, Derivation of canonical Wnt target genes in the pancreas epithelium from the RNA-seq analysis of KCT-GATA6/KDM6ADKO organoids. KCT#1-GATA6/KDM6ADKO and KCT#2-GATA6/KDM6ADKO organoids were treated with porcupine inhibitor (100 nM C59) and removal of Wnt-3A/R-spondin for two days and their transcriptomes were compared with the untreated controls. Genes with log2(fold change) > 2.5 and adjusted P value < 0.05 were selected as canonical Wnt targets and highlighted in red. c, Correlation between the enrichment of canonical Wnt target genes and the PASC signature in GSE993326 epithelial samples. r, Pearson correlation coefficient. Statistics, two-sided t-test. The black line show linear model fitting, and 95% prediction intervals are shown in grey. d, Correlation between the PASC signature samples and enrichment of Hallmark hypoxia genes in GSE993326 epithelial samples (top). Correlation between the epithelial PASC signatures and enrichment of Hallmark hypoxia genes in paired stromal samples (bottom). r, Pearson correlation coefficient. Statistics, two-sided t-test. The black line show linear model fitting, and 95% prediction intervals are shown in grey. e, The enrichment of canonical Wnt target genes and Hallmark hypoxia genes in multi-region PC samples with histological diagnosis. The data are shown for each patient (left) and with an overlay (right). Each dot indicates one sample, and the horizontal bar shows the mean. n = 41 (Glandular), 59 (Squamoid/Squamous). Box plots represent the median (centre line), upper and lower quartiles (box limits), and 1.5 × interquartile ranges (whiskers). Statistics, two-way ANOVA.

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Extended Data Fig. 9 Time-series scRNA-seq analysis of squamous reprogramming.

a, Per-condition plots of KCTS#2-GATA6KO organoids showing a gradual shift in single-cell transcriptomes. b, Marker genes for each cluster. Marker genes for each cluster were found using the FindAllMarkers function in Seurat, and the genes with average log2(fold change) > 1 are used for heatmap visualization. When the gene marked multiple clusters, the cluster with the highest fold change value was selected. For clusters with more than 200 marker genes, top 200 genes are shown. Representative genes for each cluster are shown on the right. c, The enrichment of the indicated gene sets at the indicated time points. scClassical, scBasal and intermediate co-expressor genes derive from the recent scRNA-seq analysis of metastatic PC tissues (PMID 34890551). d, Pseudotime-associated genes and their expression pattern along with the pseudotime (left). The lines show generalized additive model fitting. Gene ontology enrichment in each cluster (right). Enrichment of all gene ontology terms including GOBP, GOCC and GOMF categories were analysed. Top 5 gene sets are shown for each gene cluster.

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Extended Data Fig. 10 EZH2 inhibition in PC organoids.

a, The effects of EZH2 inhibition on organoid growth. The organoids were plated as single cells and treated with or without EZH2i (10 μM GSK126) in the presence of Wnt-3A/R-spondin. The images were captured on day 7. Scale bar, 1 mm. Represntative data from three independent culture wells with similar results. b, The effect of EZH2 and porcupine double inhibition on the growth of a PASC organoid line with a moderate response to EZH2 solo inhibition (PC64). PC64 organoids were treated with EZH2i (10 μM GSK126) and/or porcupine-i (100 nM C59) from single cells. Image capture (left) and cell viability analysis with an ATP luminescent assay (right) were performed on day 7. Data are shown as mean ± s.d. Each dot represents one culture well. Scale bar, 1 mm. Data from three biological replicates. c, Gene set enrichment analysis of the first and second principal components of EZH2i-treated and untreated PC43 transcriptomes. The genes were ranked according to the loading in each principal component, and enrichment of GOBP gene sets was analysed. Top 5 gene sets with adjusted P value < 0.05 are shown. NES, normalized enrichment score. d, Gene expression changes in PC35 following EZH2 inhibition. The organoids were treated with or without 10 μM GSK126, and RNA was harvested on day 5. Differential gene analysis was performed using edgeR. Genes with fold change > 4, adjusted P value < 0.05 are shown in red, and those with fold change < 0.25, adjusted P value < 0.05 are shown in blue. e, Gene enrichment analysis of EZH2-treated PC35 organoids vs untreated organoids. The genes were ranked according to the fold change value in edgeR analysis. Top 5 GOBP terms are presented. NES, normalized enrichment score.

Source data

Supplementary information

Reporting Summary

Supplementary Table

Clinical information, sgRNA target and primer sequences, custom gene sets, RNA-seq raw count data and mutation data from whole-exome and whole-genome sequencing used in the study.

Source data

Source Data for Figs. 1c,f, 2e,k, 3c, 4c,g, 5a,b,e, 6b–g and 7a,d, and Extended Data Figs. 2a,d, 3g, 4b, 5g, 7a,b,d,e, 8c–e, 9c,d and 10b,c,e

Statistical Source Data for Figs. 1c,f,e, 2k, 3c, 4c,g, 5a,b,e, 6b–g and 7a,d, and Extended Data Figs. 2a,d, 3g, 4b, 5g, 7a,b,d,e, 8c–e, 9c,d and 10b,c,e.

Source Data for Figs. 2a,g and 4a, and Extended Data Figs. 3b, 5c,f and 7f–j

Uncropped pseudo-gel blot images Source Data for Figs. 2a,g and 4a, and Extended Data Figs. 3b, 5c,f and 7f–j.

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Tamagawa, H., Fujii, M., Togasaki, K. et al. Wnt-deficient and hypoxic environment orchestrates squamous reprogramming of human pancreatic ductal adenocarcinoma. Nat Cell Biol 26, 1759–1772 (2024). https://doi.org/10.1038/s41556-024-01498-5

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